2024 ASEE Annual Conference & Exposition

An In-Depth Examination of Assessment Methods for Capstone Projects—Measuring Success

Presented at Assessment and Curriculum Development

In academia, data collection plays a fundamental role. It serves multiple purposes, from assessing student learning outcomes to evaluating the effectiveness of instructional methods and developing more efficient methodologies to improve the educational process. This paper explores the distinctive characteristics, purposes, and challenges inherent in data collection and organization from capstone projects, emphasizing the contrasting nature of data collection approach for regular courses.
Regular courses are from a student’s academic journey, having structured curricula and standard assessment methods. Data collected from regular courses typically focus on tracking student performance and evaluating the effectiveness of instructional strategies. The assessment data consists of quantitative measurements. It includes tools like exam scores, assignment marks, lab completion rates, attendance records, and course feedback evaluations. Frequently, data are centered on tracking students' advancement and pinpointing areas where instructional methods, curriculum design, and classroom management can be enhanced. Instructors and educational professionals employ this information to fine-tune their teaching strategies and to aid students who face challenges.
On the other hand, capstone projects reflect a conclusion of students' academic experience and emphasize the practical knowledge and skills they acquired for their future professional development. In addition, capstone projects require engaging students in the constraints of the real world to understand what it takes to achieve social value for the proposed solution, and at the same time, attain the promised performance and innovation aspects. The data derived from capstone projects typically possess a qualitative character, demanding thorough analysis. It encompasses subjective evaluations, problem-solving aptitudes, project management abilities, communication capabilities, and teamwork skills.
The data collection process for this study is conducted at the Electrical and Computer Engineering Program of a US public engineering institution. The satellite campus is situated in Qatar and adds an international dimension to the capstone projects.
This paper confronts a few challenges arising from the differing characteristics of data derived from capstone projects. Data from regular courses can be readily quantified and lend themselves to more straightforward statistical analysis. However, they may not capture the full intricacies and depth of a student's development and progress. In contrast, capstone project data provides rich qualitative, multidisciplinary, and context-driven information. However, they are more challenging to quantify and assess, requiring a detailed rubric that aligns with the capstone projects’ objectives.

Authors

Kais Abdulmawjood Texas A&M University at Qatar [biography]

Mr. Kais Abdulmawjood is expected to complete his Ph.D. in Electrical Engineering at Ontario Tech University in 2024. He received his Master of Science degree (MSc) in electrical engineering, electronics, and communication from Al-Mustansiriya University (Baghdad, Iraq) in 1998. His B.Sc. was in Electrical Engineering from Baghdad University (Baghdad, Iraq). Kais is currently a Manager for the laboratories of the Electrical and Computer Engineering program (ECEN) at Texas A&M University Qatar (TAMUQ). Before joining TAMUQ, he was a Head of the Computer Center at Al-Mustansiriya University from 1999 to 2006. In 2003, Kais was promoted to a Lecturer in the Computer and Software Engineering Department at Al-Mustansiriya University, where he continued teaching digital logic and computer and programming courses. From 1998 to 2000, he was the scientific coordinator of the Computer and Software Engineering Department at Al-Mustansiriya University. Kais also served as an adjunct faculty member at the University of Technology (Baghdad, Iraq) and as a visiting lecturer at Thimar University (Thimar, Yemen). Kais is a Senior Member of IEEE and he is a member of Eta Kappa Nu. Kais received the Dean’s Achievement Award to recognize his contribution to the Texas A&M-Qatar campus in 2021. He also received the Texas A&M University-STAR Award in 2013 and 2016.
Dr. Muhammad S. Zilany http://orcid.org/0000-0003-0181-0737 Texas A&M University at Qatar [biography]

Dr. Muhammad Zilany earned his Ph.D. in Electrical and Computer Engineering from McMaster University, Ontario, Canada, in 2007. He held academic positions at the University of Malaya and the University of Hail before joining the Electrical and Computer Engineering Program at Texas A&M University at Qatar in 2019. His research focuses on signal processing in the auditory system employing a comprehensive approach that integrates computational modeling, physiological recordings, and psychophysical studies. Dr. Zilany developed a computational model of the responses in the auditory nerve for testing our understanding of the underlying mechanical and physiological processes in the auditory periphery, which has been utilized extensively by the prominent auditory neuroscience labs in the field. Dr. Zilany is currently the chair of the ABET and Curriculum committee in the Electrical & Computer program. His commitment to nurturing the next generation of engineers and researchers underscores his role as a mentor and educator. Dr. Zilany is currently a Chartered Engineer with the Institution of Engineering and Technology (IET) in the UK, and he is also a member of the Association for Research in Otolaryngology (ARO) and the American Society for Engineering Education (ASEE).
Muna Sheet http://orcid.org/0009-0004-7447-3289 Lusail University [biography]

Ms. Muna Sheet has dedicated over two decades to academia, accumulating diverse experiences across five distinguished universities: Texas A&M University at Qatar (TAMUQ), Conestoga College in Canada, University of Toronto (UofT) in Canada, Community College of Qatar (CCQ), and Lusail University (LU) in Doha, Qatar.
She earned her Master of Science degree (MSc) in computer engineering and information technology from the University of Technology, Iraq, in 2007, followed by a Certificate in Management of Enterprise Data Analytics from the University of Toronto, Canada, in July 2017.
Currently, Muna holds the position of Lecturer at Lusail University, Qatar. Prior to this, she served as a part-time Professor at Conestoga College, Canada, and as a Solution Engineer at CONSULTEK Corporation, Canada. Muna was also a Program Coordinator and a Research Associate at Texas A&M University, Qatar, for more than five years.
Throughout her extensive career, Muna has garnered substantial expertise in instructing a wide array of courses, spanning both theoretical and practical subjects. Her teaching portfolio includes Digital Design, Big Data and Data Analysis, Mathematics, Electrical Circuits Theory, Electronics, and Control Theory.
Muna's commitment to academia extends beyond the classroom. She actively engages in various committees, contributing her insights and expertise to enhance educational processes.
Furthermore, Muna seamlessly integrates her academic prowess with her industry and research experience. She has served as a dedicated researcher at institutions such as TAMUQ and UofT, further enriching her multifaceted background.
She is a member of the IEEE.

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